# Variable Selection in Seemingly Unrelated Regressions with Random Predictors

arXiv: https://arxiv.org/abs/1605.08963 · HTML: https://mlsanity.com/paper/1605.08963/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2016.

## Built on by (most cited first, 4 of 4)

- [Bayesian subset selection and variable importance for interpretable prediction and classification](https://mlsanity.com/paper/2104.10150/) (2021)
- [Subset selection for linear mixed models](https://mlsanity.com/paper/2107.12890/) (2021)
- [Bayesian adaptive and interpretable functional regression for exposure profiles](https://mlsanity.com/paper/2203.00784/) (2022)
- [Heterogeneous Treatment Effect Estimation under Noncompliance in the Illinois Workplace Wellness Study with Bayesian Tree Ensembles](https://mlsanity.com/paper/2408.07765/) (2024)

## Abstract (arXiv)

This paper considers linear model selection when the response is vector-valued and the predictors are randomly observed. We propose a new approach that decouples statistical inference from the selection step in a "post-inference model summarization" strategy. We study the impact of predictor uncertainty on the model selection procedure. The method is demonstrated through an application to asset pricing.